A case-based CADx system is based on the idea that clinicians acquire knowledge by experience and referring to cases that they have seen before. One way, in which a decision support system can assist a clinician in making a diagnosis based on a CT scan (or any other modality scans X-rays, magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), etc.) of for example, lung cancer, is to offer previous cases that have been diagnosed and are similar to the one in question. A case-based paradigm is that pulmonary nodules similar to the one to be diagnosed are retrieved from a database of nodules with known diagnosis and presented to the radiologist. This is the basic premise of a case-based CADx system.
Case-based CADx typically involves fetching, from a database, information particular to a disease, such as tumors or lesions with known pathology, i.e., malignant or benign. The information typically includes a diagnostic scan of tumors that have already been diagnosed for visual comparison with the diagnostic scan of the tumor to be diagnosed. The tumor may be in the patient's lung, for example. A diagnostic scan of the tumor may be captured by any one of a number of imaging techniques, some of which are mentioned above. From the scan, features of a tumor may then be calculated, each feature representing a particular visual characteristic of the tumor. The tumor to be diagnosed, and the tumors of the database, can be placed in a common feature space, i.e., an N-dimensional space for which each dimension represents a respective one of N measured features. Similarity between any tumor of the database and the tumor to be diagnosed can tentatively and objectively be assessed based on proximity of the two tumors in the feature space. Typically, from the database the tumors with closest proximity are fetched as similar tumors. The fetched examples may be displayed alongside the tumor to be diagnosed, for visual comparison. Case-based CADx can also be useful in training medical personnel in diagnosing different diseases.